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Evaluating the Impact of Environmental Temperature on Global Highly Pathogenic Avian Influenza (HPAI) H5N1 Outbreaks in Domestic Poultry

Author

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  • Zhijie Zhang

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Dongmei Chen

    (Laboratory of Geographic Information and Spatial Analysis, Department of Geography, Faculty of Arts and Science, Queen's University, 99 University Avenue, Kingston, ON K7L 3N6, Canada)

  • Yue Chen

    (Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada)

  • Bo Wang

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Yi Hu

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Jie Gao

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Liqian Sun

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Rui Li

    (Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
    Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China
    Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai 200032, China)

  • Chenglong Xiong

    (Department of Microbiology and Health, School of Public Health, Fudan University, Shanghai 200032, China)

Abstract

The emergence and spread of highly pathogenic avian influenza (HPAI) A virus subtype H5N1 in Asia, Europe and Africa has had an enormously socioeconomic impact and presents an important threat to human health because of its efficient animal-to-human transmission. Many factors contribute to the occurrence and transmission of HPAI H5N1 virus, but the role of environmental temperature remains poorly understood. Based on an approach of integrating a Bayesian Cox proportional hazards model and a Besag-York-Mollié (BYM) model, we examined the specific impact of environmental temperature on HPAI H5N1 outbreaks in domestic poultry around the globe during the period from 1 December 2003 to 31 December 2009. The results showed that higher environmental temperature was a significant risk factor for earlier occurrence of HPAI H5N1 outbreaks in domestic poultry, especially for a temperature of 25 °C. Its impact varied with epidemic waves (EWs), and the magnitude of the impact tended to increase over EWs.

Suggested Citation

  • Zhijie Zhang & Dongmei Chen & Yue Chen & Bo Wang & Yi Hu & Jie Gao & Liqian Sun & Rui Li & Chenglong Xiong, 2014. "Evaluating the Impact of Environmental Temperature on Global Highly Pathogenic Avian Influenza (HPAI) H5N1 Outbreaks in Domestic Poultry," IJERPH, MDPI, vol. 11(6), pages 1-12, June.
  • Handle: RePEc:gam:jijerp:v:11:y:2014:i:6:p:6388-6399:d:37281
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    References listed on IDEAS

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    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. K. S. Li & Y. Guan & J. Wang & G. J. D. Smith & K. M. Xu & L. Duan & A. P. Rahardjo & P. Puthavathana & C. Buranathai & T. D. Nguyen & A. T. S. Estoepangestie & A. Chaisingh & P. Auewarakul & H. T. Lo, 2004. "Genesis of a highly pathogenic and potentially pandemic H5N1 influenza virus in eastern Asia," Nature, Nature, vol. 430(6996), pages 209-213, July.
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    Cited by:

    1. Xin-Lou Li & Kun Liu & Hong-Wu Yao & Ye Sun & Wan-Jun Chen & Ruo-Xi Sun & Sake J. De Vlas & Li-Qun Fang & Wu-Chun Cao, 2015. "Highly Pathogenic Avian Influenza H5N1 in Mainland China," IJERPH, MDPI, vol. 12(5), pages 1-20, May.

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